class_docstr: This class handles iteration events to save the model as checkpoint files.
class_docstr_tok: ["This","class","handles","iteration","events","to","save","the","model","as","checkpoint","files","."]
docstr: Saving the model according to the frequency of ``save_every_n``.
  
  :param _sender:
  :param msg: an iteration message instance
  :return:
docstr_tok: ["Saving","the","model","according","to","the","frequency","of","``","save_every_n","``",".",":","param","_sender",":",":","param","msg",":","an","iteration","message","instance",":","return",":"]
code: def save_model_interval(self, _sender, **msg):
      """
      Saving the model according to the frequency of ``save_every_n``.
  
      :param _sender:
      :param msg: an iteration message instance
      :return:
      """
      if not msg["iter_msg"].is_training:
          return
      iter_i = msg["iter_msg"].current_iter
      if iter_i > 0 and iter_i % self.save_every_n == 0:
          self._save_at(iter_i)
  
file_path: NiftyNet/niftynet/engine/handler_model.py
imports: import os
  import tensorflow
  from niftynet.engine.application_variables import global_vars_init_or_restore
  from niftynet.engine.signal import ITER_FINISHED, SESS_FINISHED, SESS_STARTED
  from niftynet.io.misc_io import touch_folder
label_desc: Use image node_id 4 for calling the ModelSaver obj's underlying member method code with example usage: obj.save_model_interval(_sender) without return types